Search result: Catalogue data in Autumn Semester 2018
|Robotics, Systems and Control Master|
|151-0107-20L||High Performance Computing for Science and Engineering (HPCSE) I||W||4 credits||4G||P. Koumoutsakos|
|Abstract||This course gives an introduction into algorithms and numerical methods for parallel computing for multi and many-core architectures and for applications from problems in science and engineering.|
|Objective||Introduction to HPC for scientists and engineers|
1. Parallel Computing Architectures
|Content||Parallel Programming models and languages (OpenMP, MPI). Parallel Performance metrics and Code Optimization. Examples based on grid and particle methods for solving Partial Differential Equations and on fundamentals of stochastic optimisation and machine learning.|
Class notes, handouts
|151-0323-00L||Autonomous Mobility on Demand: From Car to Fleet |
Number of participants limited to 20.
|W||4 credits||4G||J. Tani, A. Censi|
|Abstract||Autonomous Mobility on Demand systems based on self-driving cars will make a huge impact in the world. This class describes the basics of modeling, perception, learning, planning, and control for fleets of self-driving cars. We focus particular regard to the problem of integration and co-design of components and behaviors. The course has a heavy experimental component.|
|Objective||The students will learn how to create all parts of an architecture for a complex multi-robot system performing a nontrivial task (an autonomous taxi service).|
|Content||Part 1: Single car functionalities (perception-planning-control loop, based on vision data); Part 2: Multiple cars (formal methods for safety, platooning, coordination, fleet-level policy optimization)|
|Lecture notes||Course notes will be provided for free in an electronic form.|
|Literature||Course notes will be provided for free in an electronic form. These are some books that can be used to provide background information or consulted as references: (1) Siegwart, Nourbakhsh, Scaramuzza - Introduction to autonomous mobile robots; (2) Norvig, Russell - Artificial Intelligent, a modern approach. (3) Peter Corke - Robotics Vision and Control (4) Oussama Khatib, Bruno Siciliano - Handbook of Robotics|
|Prerequisites / Notice||This course is also known as Duckietown. Students should have taken a basic course in probability, and should be familiar with basic programming and Linux use.|
|151-0509-00L||Microscale Acoustofluidics |
Number of participants limited to 30.
|W||4 credits||3G||J. Dual|
|Abstract||In this lecture the basics as well as practical aspects (from modelling to design and fabrication ) are described from a solid and fluid mechanics perspective with applications to microsystems and lab on a chip devices.|
|Objective||Understanding acoustophoresis, the design of devices and potential applications|
|Content||Linear and nonlinear acoustics, foundations of fluid and solid mechanics and piezoelectricity, Gorkov potential, numerical modelling, acoustic streaming, applications from ultrasonic microrobotics to surface acoustic wave devices|
|Lecture notes||Yes, incl. Chapters from the Tutorial: Microscale Acoustofluidics, T. Laurell and A. Lenshof, Ed., Royal Society of Chemistry, 2015|
|Literature||Microscale Acoustofluidics, T. Laurell and A. Lenshof, Ed., Royal Society of Chemistry, 2015|
|Prerequisites / Notice||Solid and fluid continuum mechanics. Notice: The exercise part is a mixture of presentation, lab sessions ( both compulsary) and hand in homework.|
|151-0563-01L||Dynamic Programming and Optimal Control||W||4 credits||2V + 1U||R. D'Andrea|
|Abstract||Introduction to Dynamic Programming and Optimal Control.|
|Objective||Covers the fundamental concepts of Dynamic Programming & Optimal Control.|
|Content||Dynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control.|
|Literature||Dynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover.|
|Prerequisites / Notice||Requirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra.|
|151-0593-00L||Embedded Control Systems||W||4 credits||6G||J. S. Freudenberg, M. Schmid Daners|
|Abstract||This course provides a comprehensive overview of embedded control systems. The concepts introduced are implemented and verified on a microprocessor-controlled haptic device.|
|Objective||Familiarize students with main architectural principles and concepts of embedded control systems.|
|Content||An embedded system is a microprocessor used as a component in another piece of technology, such as cell phones or automobiles. In this intensive two-week block course the students are presented the principles of embedded digital control systems using a haptic device as an example for a mechatronic system. A haptic interface allows for a human to interact with a computer through the sense of touch.|
Subjects covered in lectures and practical lab exercises include:
- The application of C-programming on a microprocessor
- Digital I/O and serial communication
- Quadrature decoding for wheel position sensing
- Queued analog-to-digital conversion to interface with the analog world
- Pulse width modulation
- Timer interrupts to create sampling time intervals
- System dynamics and virtual worlds with haptic feedback
- Introduction to rapid prototyping
|Lecture notes||Lecture notes, lab instructions, supplemental material|
|Prerequisites / Notice||Prerequisite courses are Control Systems I and Informatics I.|
This course is restricted to 33 students due to limited lab infrastructure. Interested students please contact Marianne Schmid (E-Mail: firstname.lastname@example.org)
After your reservation has been confirmed please register online at www.mystudies.ethz.ch.
Detailed information can be found on the course website
|151-0601-00L||Theory of Robotics and Mechatronics||W||4 credits||3G||P. Korba, S. Stoeter|
|Abstract||This course provides an introduction and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control. It’s a requirement for the Robotics Vertiefung and for the Masters in Mechatronics and Microsystems.|
|Objective||Robotics is often viewed from three perspectives: perception (sensing), manipulation (affecting changes in the world), and cognition (intelligence). Robotic systems integrate aspects of all three of these areas. This course provides an introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control. This course is a requirement for the Robotics Vertiefung and for the Masters in Mechatronics and Microsystems.|
|Content||An introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control.|
|151-0604-00L||Microrobotics||W||4 credits||3G||B. Nelson|
|Abstract||Microrobotics is an interdisciplinary field that combines aspects of robotics, micro and nanotechnology, biomedical engineering, and materials science. The aim of this course is to expose students to the fundamentals of this emerging field. Throughout the course, the students apply these concepts in assignments. The course concludes with an end-of-semester examination.|
|Objective||The objective of this course is to expose students to the fundamental aspects of the emerging field of microrobotics. This includes a focus on physical laws that predominate at the microscale, technologies for fabricating small devices, bio-inspired design, and applications of the field.|
|Content||Main topics of the course include:|
- Scaling laws at micro/nano scales
- Low Reynolds number flows
- Observation tools
- Materials and fabrication methods
- Applications of biomedical microrobots
|Lecture notes||The powerpoint slides presented in the lectures will be made available as pdf files. Several readings will also be made available electronically.|
|Prerequisites / Notice||The lecture will be taught in English.|
|151-0632-00L||Vision Algorithms for Mobile Robotics |
Number of participants limited to 55
Registration is on a first come, first served basis and SPACE IS LIMITED!
|W||4 credits||2V + 2U||D. Scaramuzza|
|Abstract||For a robot to be autonomous, it has to perceive and understand the world around it. This course introduces you to the key computer vision algorithms used in mobile robotics, such as feature extraction, multiple view geometry, dense reconstruction, tracking, image retrieval, event-based vision, and visual-inertial odometry (the algorithms behind Google Tango, Ms Hololens, and the Mars rovers).|
|Objective||Learn the fundamental computer vision algorithms used in mobile robotics, in particular: feature extraction, multiple view geometry, dense reconstruction, object tracking, image retrieval, event-based vision, and visual-inertial odometry (the algorithm behind Google Tango).|
|Content||Each lecture will be followed by a lab session where you will learn to implement the building block of a visual odometry algorithm in Matlab. By the end of the course, you will integrate all these building blocks into a working visual odometry algorithm.|
|Lecture notes||Lecture slides will be made available on the course official website: http://rpg.ifi.uzh.ch/teaching.html|
|Literature|| Computer Vision: Algorithms and Applications, by Richard Szeliski, Springer, 2010. |
 Robotics Vision and Control: Fundamental Algorithms, by Peter Corke 2011.
 An Invitation to 3D Vision, by Y. Ma, S. Soatto, J. Kosecka, S.S. Sastry.
 Multiple view Geometry, by R. Hartley and A. Zisserman.
 Introduction to autonomous mobile robots 2nd Edition, by R. Siegwart, I.R. Nourbakhsh, and D. Scaramuzza, February, 2011
|Prerequisites / Notice||Fundamentals of algebra, geomertry, matrix calculus, and Matlab programming.|
|151-0851-00L||Robot Dynamics||W||4 credits||2V + 2U||M. Hutter, R. Siegwart|
|Abstract||We will provide an overview on how to kinematically and dynamically model typical robotic systems such as robot arms, legged robots, rotary wing systems, or fixed wing.|
|Objective||The primary objective of this course is that the student deepens an applied understanding of how to model the most common robotic systems. The student receives a solid background in kinematics, dynamics, and rotations of multi-body systems. On the basis of state of the art applications, he/she will learn all necessary tools to work in the field of design or control of robotic systems.|
|Content||The course consists of three parts: First, we will refresh and deepen the student's knowledge in kinematics, dynamics, and rotations of multi-body systems. In this context, the learning material will build upon the courses for mechanics and dynamics available at ETH, with the particular focus on their application to robotic systems. The goal is to foster the conceptual understanding of similarities and differences among the various types of robots. In the second part, we will apply the learned material to classical robotic arms as well as legged systems and discuss kinematic constraints and interaction forces. In the third part, focus is put on modeling fixed wing aircraft, along with related design and control concepts. In this context, we also touch aerodynamics and flight mechanics to an extent typically required in robotics. The last part finally covers different helicopter types, with a focus on quadrotors and the coaxial configuration which we see today in many UAV applications. Case studies on all main topics provide the link to real applications and to the state of the art in robotics.|
|Prerequisites / Notice||The contents of the following ETH Bachelor lectures or equivalent are assumed to be known: Mechanics and Dynamics, Control, Basics in Fluid Dynamics.|
|151-1116-00L||Introduction to Aircraft and Car Aerodynamics||W||4 credits||3G||J. Wildi|
|Abstract||Aircraft aerodynamics: Atmosphere; aerodynamic forces (lift, drag); thrust.|
Vehicle aerodynamics: Aerodynamic and mass forces, drag, lift, car aerodynamics and performence. Passenger cars, trucks, racing cars.
|Objective||An introduction to the basic principles and interrelationships of aircraft and automotive aerodynamics.|
To understand the basic relations of the origin of aerodynamic forces (ie lift, drag). To quantify the aerodynamic forces for basic configurations of aercraft and car components.
Illustration of the intrinsic problems and results using examples.
Using experimental and theoretical methods to illustrate possibilities and limits.
|Content||Aircraft aerodynamics: atmosphere, aerodynamic forces (ascending force: profile, wings. Resistance, residual resistance, induced resistance); thrust (overview of the propulsion system, aerodynamics of the propellers), introduction to static longitudinal stability.|
Automobile aerodynamics: Basic principles: aerodynamic force and the force of inertia, resistance, drive, aerodynamic and driving performance. Cars commercial vehicles, racing cars.
|Lecture notes||1.) Grundlagen der Flugtechnik (Basics of flight science, script in german language)|
2.) Einführung in die Fahrzeugaerodynamik (Introduction in car aerodynamics, script in german language)
|Literature||English literature covering the content of the course:|
- Anderson Jr, John D: Introduction to Flight, Mc Graw Hill, Ed 06, 2007; ISBN: 9780073529394
- Mc Cormick, B.W.: Aerodynamics, Aeronautics and Flight Mechanics, John Wiley and Sons, 1979
- Hucho, Wolf-Heinrich: Aerodynamics of Road Vehicles, SAE International, 1998
|151-0532-00L||Nonlinear Dynamics and Chaos I||W||4 credits||2V + 2U||F. Kogelbauer|
|Abstract||Basic facts about nonlinear systems; stability and near-equilibrium dynamics; bifurcations; dynamical systems on the plane; non-autonomous dynamical systems; chaotic dynamics.|
|Objective||This course is intended for Masters and Ph.D. students in engineering sciences, physics and applied mathematics who are interested in the behavior of nonlinear dynamical systems. It offers an introduction to the qualitative study of nonlinear physical phenomena modeled by differential equations or discrete maps. We discuss applications in classical mechanics, electrical engineering, fluid mechanics, and biology. A more advanced Part II of this class is offered every other year.|
|Content||(1) Basic facts about nonlinear systems: Existence, uniqueness, and dependence on initial data.|
(2) Near equilibrium dynamics: Linear and Lyapunov stability
(3) Bifurcations of equilibria: Center manifolds, normal forms, and elementary bifurcations
(4) Nonlinear dynamical systems on the plane: Phase plane techniques, limit sets, and limit cycles.
(5) Time-dependent dynamical systems: Floquet theory, Poincare maps, averaging methods, resonance
|Lecture notes||The class lecture notes will be posted electronically after each lecture. Students should not rely on these but prepare their own notes during the lecture.|
|Prerequisites / Notice||- Prerequisites: Analysis, linear algebra and a basic course in differential equations.|
- Exam: two-hour written exam in English.
- Homework: A homework assignment will be due roughly every other week. Hints to solutions will be posted after the homework due dates.
|227-0102-00L||Discrete Event Systems||W||6 credits||4G||L. Thiele, L. Vanbever, R. Wattenhofer|
|Abstract||Introduction to discrete event systems. We start out by studying popular models of discrete event systems. In the second part of the course we analyze discrete event systems from an average-case and from a worst-case perspective. Topics include: Automata and Languages, Specification Models, Stochastic Discrete Event Systems, Worst-Case Event Systems, Verification, Network Calculus.|
|Objective||Over the past few decades the rapid evolution of computing, communication, and information technologies has brought about the proliferation of new dynamic systems. A significant part of activity in these systems is governed by operational rules designed by humans. The dynamics of these systems are characterized by asynchronous occurrences of discrete events, some controlled (e.g. hitting a keyboard key, sending a message), some not (e.g. spontaneous failure, packet loss). |
The mathematical arsenal centered around differential equations that has been employed in systems engineering to model and study processes governed by the laws of nature is often inadequate or inappropriate for discrete event systems. The challenge is to develop new modeling frameworks, analysis techniques, design tools, testing methods, and optimization processes for this new generation of systems.
In this lecture we give an introduction to discrete event systems. We start out the course by studying popular models of discrete event systems, such as automata and Petri nets. In the second part of the course we analyze discrete event systems. We first examine discrete event systems from an average-case perspective: we model discrete events as stochastic processes, and then apply Markov chains and queuing theory for an understanding of the typical behavior of a system. In the last part of the course we analyze discrete event systems from a worst-case perspective using the theory of online algorithms and adversarial queuing.
2. Automata and Languages
3. Smarter Automata
4. Specification Models
5. Stochastic Discrete Event Systems
6. Worst-Case Event Systems
7. Network Calculus
|Literature||[bertsekas] Data Networks |
Dimitri Bersekas, Robert Gallager
Prentice Hall, 1991, ISBN: 0132009161
[borodin] Online Computation and Competitive Analysis
Allan Borodin, Ran El-Yaniv.
Cambridge University Press, 1998
[boudec] Network Calculus
J.-Y. Le Boudec, P. Thiran
[cassandras] Introduction to Discrete Event Systems
Christos Cassandras, Stéphane Lafortune.
Kluwer Academic Publishers, 1999, ISBN 0-7923-8609-4
[fiat] Online Algorithms: The State of the Art
A. Fiat and G. Woeginger
[hochbaum] Approximation Algorithms for NP-hard Problems (Chapter 13 by S. Irani, A. Karlin)
[schickinger] Diskrete Strukturen (Band 2: Wahrscheinlichkeitstheorie und Statistik)
T. Schickinger, A. Steger
Springer, Berlin, 2001
[sipser] Introduction to the Theory of Computation
PWS Publishing Company, 1996, ISBN 053494728X
|227-0103-00L||Control Systems||W||6 credits||2V + 2U||F. Dörfler|
|Abstract||Study of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input - single output and multivariable systems.|
|Objective||Study of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input - single output and multivariable systems.|
|Content||Process automation, concept of control. Modelling of dynamical systems - examples, state space description, linearisation, analytical/numerical solution. Laplace transform, system response for first and second order systems - effect of additional poles and zeros. Closed-loop control - idea of feedback. PID control, Ziegler - Nichols tuning. Stability, Routh-Hurwitz criterion, root locus, frequency response, Bode diagram, Bode gain/phase relationship, controller design via "loop shaping", Nyquist criterion. Feedforward compensation, cascade control. Multivariable systems (transfer matrix, state space representation), multi-loop control, problem of coupling, Relative Gain Array, decoupling, sensitivity to model uncertainty. State space representation (modal description, controllability, control canonical form, observer canonical form), state feedback, pole placement - choice of poles. Observer, observability, duality, separation principle. LQ Regulator, optimal state estimation.|
|Literature||K. J. Aström & R. Murray. Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, 2010.|
R. C. Dorf and R. H. Bishop. Modern Control Systems. Prentice Hall, New Jersey, 2007.
G. F. Franklin, J. D. Powell, and A. Emami-Naeini. Feedback Control of Dynamic Systems. Addison-Wesley, 2010.
J. Lunze. Regelungstechnik 1. Springer, Berlin, 2014.
J. Lunze. Regelungstechnik 2. Springer, Berlin, 2014.
|Prerequisites / Notice||Prerequisites: Signal and Systems Theory II. |
MATLAB is used for system analysis and simulation.
|227-0225-00L||Linear System Theory||W||6 credits||5G||M. Kamgarpour|
|Abstract||The class is intended to provide a comprehensive overview of the theory of linear dynamical systems, stability analysis, and their use in control and estimation. The focus is on the mathematics behind the physical properties of these systems and on understanding and constructing proofs of properties of linear control systems.|
|Objective||Students should be able to apply the fundamental results in linear system theory to analyze and control linear dynamical systems.|
|Content||- Proof techniques and practices.|
- Linear spaces, normed linear spaces and Hilbert spaces.
- Ordinary differential equations, existence and uniqueness of solutions.
- Continuous and discrete-time, time-varying linear systems. Time domain solutions. Time invariant systems treated as a special case.
- Controllability and observability, duality. Time invariant systems treated as a special case.
- Stability and stabilization, observers, state and output feedback, separation principle.
|Lecture notes||Available on the course Moodle platform.|
|Prerequisites / Notice||Sufficient mathematical maturity with special focus on logic, linear algebra, analysis.|
|227-0247-00L||Power Electronic Systems I||W||6 credits||4G||J. W. Kolar|
|Abstract||Basics of the switching behavior, gate drive and snubber circuits of power semiconductors are discussed. Soft-switching and resonant DC/DC converters are analyzed in detail and high frequency loss mechanisms of magnetic components are explained. Space vector modulation of three-phase inverters is introduced and the main power components are designed for typical industry applications.|
|Objective||Detailed understanding of the principle of operation and modulation of advanced power electronics converter systems, especially of zero voltage switching and zero current switching non-isolated and isolated DC/DC converter systems and three-phase voltage DC link inverter systems. Furthermore, the course should convey knowledge on the switching frequency related losses of power semiconductors and inductive power components and introduce the concept of space vector calculus which provides a basis for the comprehensive discussion of three-phase PWM converters systems in the lecture Power Electronic Systems II.|
|Content||Basics of the switching behavior and gate drive circuits of power semiconductor devices and auxiliary circuits for minimizing the switching losses are explained. Furthermore, zero voltage switching, zero current switching, and resonant DC/DC converters are discussed in detail; the operating behavior of isolated full-bridge DC/DC converters is detailed for different secondary side rectifier topologies; high frequency loss mechanisms of magnetic components of converter circuits are explained and approximate calculation methods are presented; the concept of space vector calculus for analyzing three-phase systems is introduced; finally, phase-oriented and space vector modulation of three-phase inverter systems are discussed related to voltage DC link inverter systems and the design of the main power components based on analytical calculations is explained.|
|Lecture notes||Lecture notes and associated exercises including correct answers, simulation program for interactive self-learning including visualization/animation features.|
|Prerequisites / Notice||Prerequisites: Introductory course on power electronics.|
|227-0447-00L||Image Analysis and Computer Vision||W||6 credits||3V + 1U||L. Van Gool, O. Göksel, E. Konukoglu|
|Abstract||Light and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks.|
|Objective||Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.|
|Content||This course aims at offering a self-contained account of computer vision and its underlying concepts, including the recent use of deep learning.|
The first part starts with an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First the interaction of light with matter is considered. The most important hardware components such as cameras and illumination sources are also discussed. The course then turns to image discretization, necessary to process images by computer.
The next part describes necessary pre-processing steps, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and 3D shape as two important examples. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed. A major part at the end is devoted to deep learning and AI-based approaches to image analysis. Its main focus is on object recognition, but also other examples of image processing using deep neural nets are given.
|Lecture notes||Course material Script, computer demonstrations, exercises and problem solutions|
|Prerequisites / Notice||Prerequisites: |
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux.
The course language is English.
|227-0526-00L||Power System Analysis||W||6 credits||4G||G. Hug|
|Abstract||The goal of this course is understanding the stationary and dynamic problems in electrical power systems. The course includes the development of stationary models of the electrical network, their mathematical representation and special characteristics and solution methods of large linear and non-linear systems of equations related to electrical power networks.|
|Objective||The goal of this course is understanding the stationary and dynamic problems in electrical power systems and the application of analysis tools in steady and dynamic states.|
|Content||The course includes the development of stationary models of the electrical network, their mathematical representation and special characteristics and solution methods of large linear and non-linear systems of equations related to electrical power grids. Approaches such as the Newton-Raphson algorithm applied to power flow equations, superposition technique for short-circuit analysis, equal area criterion and nose curve analysis are discussed as well as power flow computation techniques for distribution grids.|
|Lecture notes||Lecture notes.|
|227-0689-00L||System Identification||W||4 credits||2V + 1U||R. Smith|
|Abstract||Theory and techniques for the identification of dynamic models from experimentally obtained system input-output data.|
|Objective||To provide a series of practical techniques for the development of dynamical models from experimental data, with the emphasis being on the development of models suitable for feedback control design purposes. To provide sufficient theory to enable the practitioner to understand the trade-offs between model accuracy, data quality and data quantity.|
|Content||Introduction to modeling: Black-box and grey-box models; Parametric and non-parametric models; ARX, ARMAX (etc.) models.|
Predictive, open-loop, black-box identification methods. Time and frequency domain methods. Subspace identification methods.
Optimal experimental design, Cramer-Rao bounds, input signal design.
Parametric identification methods. On-line and batch approaches.
Closed-loop identification strategies. Trade-off between controller performance and information available for identification.
|Literature||"System Identification; Theory for the User" Lennart Ljung, Prentice Hall (2nd Ed), 1999.|
"Dynamic system identification: Experimental design and data analysis", GC Goodwin and RL Payne, Academic Press, 1977.
|Prerequisites / Notice||Control systems (227-0216-00L) or equivalent.|
|227-0697-00L||Industrial Process Control||W||4 credits||3G||M. Mercangöz, A. Horch|
|Abstract||Introduction to process automation and its application in process industry and power generation|
|Objective||Knowledge of process automation and its application in industry and power generation|
|Content||Introduction to process automation: system architecture, data handling, communication (fieldbusses), process visualization, engineering, etc.|
Analysis and design of open loop control problems: discrete automata, decision tables, petri-nets, drive control and object oriented function group automation philosophy, RT-UML.
Engineering: Application programming in IEC61131-3 (function blocks, sequence control, structured text); process visualization and operation; engineering integration from sensor, cabling, topology design, function, visualization, diagnosis, to documentation; Industry standards (e.g. OPC, Profibus); Ergonomic design, safety (IEC61508) and availability, supervision and diagnosis.
Practical examples from process industry, power generation and newspaper production.
|Lecture notes||Slides will be available as .PDF documents, see "Learning materials" (for registered students only)|
|Prerequisites / Notice||Exercises: Tuesday 15-16|
Practical exercises will illustrate some topics, e.g. some control software coding using industry standard programming tools based on IEC61131-3.
|227-0778-00L||Hardware/Software Codesign||W||6 credits||2V + 2U||L. Thiele|
|Abstract||The course provides advanced knowledge in the design of complex computer systems, in particular embedded systems. Models and methods are discussed that are fundamental for systems that consist of software and hardware components.|
|Objective||The course provides advanced knowledge in the design of complex computer systems, in particular embedded systems. Models and methods are discussed that are fundamental for systems that consist of software and hardware components.|
|Content||The course covers the following subjects: (a) Models for describing hardware and software components (specification), (b) Hardware-Software Interfaces (instruction set, hardware and software components, reconfigurable computing, heterogeneous computer architectures, System-on-Chip), (c) Application specific instruction sets, code generation and retargetable compilation, (d) Performance analysis and estimation techniques, (e) System design (hardware-software partitioning and design space exploration).|
|Lecture notes||Material for exercises, copies of transparencies.|
|Literature||Peter Marwedel, Embedded System Design, Springer, ISBN-13 978-94-007-0256-1, 2011.|
Wayne Wolf. Computers as Components. Morgan Kaufmann, ISBN-13: 978-0123884367, 2012.
|Prerequisites / Notice||Prerequisites for the course is a basic knowledge in the following areas: computer architecture, digital design, software design, embedded systems|
|227-0920-00L||Seminar in Systems and Control||Z||0 credits||1S||F. Dörfler, R. D'Andrea, E. Frazzoli, M. H. Khammash, J. Lygeros, R. Smith|
|Abstract||Current topics in Systems and Control presented mostly by external speakers from academia and industry|
|252-0535-00L||Advanced Machine Learning||W||8 credits||3V + 2U + 2A||J. M. Buhmann|
|Abstract||Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.|
|Objective||Students will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data.|
|Content||The theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.|
Topics covered in the lecture include:
What is data?
Computational learning theory
Ensembles: Bagging and Boosting
Max Margin methods
Dimensionality reduction techniques
Non-parametric density estimation
Learning Dynamical Systems
|Lecture notes||No lecture notes, but slides will be made available on the course webpage.|
|Literature||C. Bishop. Pattern Recognition and Machine Learning. Springer 2007.|
R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley &
Sons, second edition, 2001.
T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Springer, 2001.
L. Wasserman. All of Statistics: A Concise Course in Statistical
Inference. Springer, 2004.
|Prerequisites / Notice||The course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments.|
Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution.
|252-3110-00L||Human Computer Interaction |
Number of participants limited to 150.
|W||4 credits||2V + 1U||O. Hilliges|
|Abstract||The course provides an introduction to the field of human-computer interaction, emphasising the central role of the user in system design. Through detailed case studies, students will be introduced to different methods used to analyse the user experience and shown how these can inform the design of new interfaces, systems and technologies.|
|Objective||The goal of the course is that students should understand the principles of user-centred design and be able to apply these in practice.|
|Content||The course will introduce students to various methods of analysing the user experience, showing how these can be used at different stages of system development from requirements analysis through to usability testing. Students will get experience of designing and carrying out user studies as well as analysing results. The course will also cover the basic principles of interaction design. Practical exercises related to touch and gesture-based interaction will be used to reinforce the concepts introduced in the lecture. To get students to further think beyond traditional system design, we will discuss issues related to ambient information and awareness.|
|252-5051-00L||Advanced Topics in Machine Learning |
Number of participants limited to 40.
The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
|W||2 credits||2S||J. M. Buhmann, A. Krause, G. Rätsch|
|Abstract||In this seminar, recent papers of the pattern recognition and machine learning literature are presented and discussed. Possible topics cover statistical models in computer vision, graphical models and machine learning.|
|Objective||The seminar "Advanced Topics in Machine Learning" familiarizes students with recent developments in pattern recognition and machine learning. Original articles have to be presented and critically reviewed. The students will learn how to structure a scientific presentation in English which covers the key ideas of a scientific paper. An important goal of the seminar presentation is to summarize the essential ideas of the paper in sufficient depth while omitting details which are not essential for the understanding of the work. The presentation style will play an important role and should reach the level of professional scientific presentations.|
|Content||The seminar will cover a number of recent papers which have emerged as important contributions to the pattern recognition and machine learning literature. The topics will vary from year to year but they are centered on methodological issues in machine learning like new learning algorithms, ensemble methods or new statistical models for machine learning applications. Frequently, papers are selected from computer vision or bioinformatics - two fields, which relies more and more on machine learning methodology and statistical models.|
|Literature||The papers will be presented in the first session of the seminar.|
|252-5701-00L||Advanced Topics in Computer Graphics and Vision |
Number of participants limited to 24.
The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
|W||2 credits||2S||M. Gross, M. Pollefeys, O. Sorkine Hornung|
|Abstract||This seminar covers advanced topics in computer graphics, such as modeling, rendering, animation, real-time graphics, physical simulation, and computational photography. Each time the course is offered, a collection of research papers is selected and each student presents one paper to the class and leads a discussion about the paper and related topics.|
|Objective||The goal is to get an in-depth understanding of actual problems and research topics in the field of computer graphics as well as improve presentations and critical analysis skills.|
|Content||This seminar covers advanced topics in computer graphics,|
including both seminal research papers as well as the latest
research results. Each time the course is offered, a collection of
research papers are selected covering topics such as modeling,
rendering, animation, real-time graphics, physical simulation, and
computational photography. Each student presents one paper to the
class and leads a discussion about the paper and related topics.
All students read the papers and participate in the discussion.
|Lecture notes||no script|
|Literature||Individual research papers are selected each term. See http://graphics.ethz.ch/ for the current list.|
|Prerequisites / Notice||Prerequisites: |
The courses "Computer Graphics I and II" (GDV I & II) are recommended, but not mandatory.
|263-5210-00L||Probabilistic Artificial Intelligence||W||4 credits||2V + 1U||A. Krause|
|Abstract||This course introduces core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet.|
|Objective||How can we build systems that perform well in uncertain environments and unforeseen situations? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. The course is designed for upper-level undergraduate and graduate students.|
- Search (BFS, DFS, A*), constraint satisfaction and optimization
- Tutorial in logic (propositional, first-order)
- Bayesian Networks (models, exact and approximative inference, learning) - Temporal models (Hidden Markov Models, Dynamic Bayesian Networks)
- Probabilistic palnning (MDPs, POMPDPs)
- Reinforcement learning
- Combining logic and probability
|Prerequisites / Notice||Solid basic knowledge in statistics, algorithms and programming|
|263-5902-00L||Computer Vision||W||6 credits||3V + 1U + 1A||M. Pollefeys, V. Ferrari, L. Van Gool|
|Abstract||The goal of this course is to provide students with a good understanding of computer vision and image analysis techniques. The main concepts and techniques will be studied in depth and practical algorithms and approaches will be discussed and explored through the exercises.|
|Objective||The objectives of this course are:|
1. To introduce the fundamental problems of computer vision.
2. To introduce the main concepts and techniques used to solve those.
3. To enable participants to implement solutions for reasonably complex problems.
4. To enable participants to make sense of the computer vision literature.
|Content||Camera models and calibration, invariant features, Multiple-view geometry, Model fitting, Stereo Matching, Segmentation, 2D Shape matching, Shape from Silhouettes, Optical flow, Structure from motion, Tracking, Object recognition, Object category recognition|
|Prerequisites / Notice||It is recommended that students have taken the Visual Computing lecture or a similar course introducing basic image processing concepts before taking this course.|
|376-1279-00L||Virtual and Augmented Reality in Medicine||W||3 credits||2V||R. Riener, O. Göksel, M. Harders|
|Abstract||Virtual and Augmented Reality can support applications in medicine, e.g. for training, planning or therapy. This lecture derives the technical principles of multimodal (audiovisual, haptic, etc.) input devices, displays, and rendering techniques. Examples are presented in the fields of surgical training, intra-operative support, and rehabilitation. The lecture is accompanied by lab demonstrations.|
|Objective||Provide theoretical and practical knowledge of new principles and applications of multi-modal simulation and interface technologies in medical education, therapy, and rehabilitation.|
|Content||Virtual and Augmented Reality have the potential to provide descriptive and practical information for medical applications, while relieving the patient and/or the physician. Multi-modal interactions between the user and the virtual environment facilitate the generation of high-fidelity sensory impressions, by using visual, haptic, and auditory modalities. On the basis of the existing physiological constraints, this lecture derives the technical requirements and principles of multi-modal input devices, displays, and rendering techniques. Several examples are presented that are currently being developed or already applied, for instance in surgical training, intra-operative augmentation, and rehabilitation. The lecture will be accompanied by visits to facilities equipped with current VR and AR equipment.|
|Literature||Recommended readings will be announced in the lecture. Selected books covering some of the presented topics are: |
• Virtual Reality in Medicine. Riener, Robert; Harders, Matthias; 2012 Springer.
• Augmented Reality: Principles and Practice (Usability). Schmalstieg, Dieter; Hollerer, Tobias; 2016 Pearson.
• Real-Time Volume Graphics. Rezk-Salama, Christof; Engel, Klaus; Hadwiger, Markus; Kniss, Joe; Weiskopf, Daniel; 2006 Taylor & Francis.
• Haptic Rendering: Foundations, Algorithms, and Applications. Lin, Ming; Otaduy, Miguel; 2008 CRC Press.
• Developing Virtual Reality Applications: Foundations of Effective Design. Craig , Alan; Sherman, William; Will, Jeffrey; 2009 Morgan Kaufmann.
|Prerequisites / Notice||Notice The course language is English. Any further details will be announced in the first lecture. |
The general target group is students of higher semesters as well as PhD students of D-HEST, D-MAVT, D-ITET, D-INFK, D-PHYS. Students of other departments, faculties, and courses are also welcome.
|376-1504-00L||Physical Human Robot Interaction (pHRI) |
Number of participants limited to 21.
|W||4 credits||2V + 2U||R. Gassert, O. Lambercy|
|Abstract||This course focuses on the emerging, interdisciplinary field of physical human-robot interaction, bringing together themes from robotics, real-time control, human factors, haptics, virtual environments, interaction design and other fields to enable the development of human-oriented robotic systems.|
|Objective||The objective of this course is to give an introduction to the fundamentals of physical human robot interaction, through lectures on the underlying theoretical/mechatronics aspects and application fields, in combination with a hands-on lab tutorial. The course will guide students through the design and evaluation process of such systems.|
By the end of this course, you should understand the critical elements in human-robot interactions - both in terms of engineering and human factors - and use these to evaluate and de- sign safe and efficient assistive and rehabilitative robotic systems. Specifically, you should be able to:
1) identify critical human factors in physical human-robot interaction and use these to derive design requirements;
2) compare and select mechatronic components that optimally fulfill the defined design requirements;
3) derive a model of the device dynamics to guide and optimize the selection and integration of selected components
into a functional system;
4) design control hardware and software and implement and
test human-interactive control strategies on the physical
5) characterize and optimize such systems using both engineering and psychophysical evaluation metrics;
6) investigate and optimize one aspect of the physical setup and convey and defend the gained insights in a technical presentation.
|Content||This course provides an introduction to fundamental aspects of physical human-robot interaction. After an overview of human haptic, visual and auditory sensing, neurophysiology and psychophysics, principles of human-robot interaction systems (kinematics, mechanical transmissions, robot sensors and actuators used in these systems) will be introduced. Throughout the course, students will gain knowledge of interaction control strategies including impedance/admittance and force control, haptic rendering basics and issues in device design for humans such as transparency and stability analysis, safety hardware and procedures. The course is organized into lectures that aim to bring students up to speed with the basics of these systems, readings on classical and current topics in physical human-robot interaction, laboratory sessions and lab visits. |
Students will attend periodic laboratory sessions where they will implement the theoretical aspects learned during the lectures. Here the salient features of haptic device design will be identified and theoretical aspects will be implemented in a haptic system based on the haptic paddle (Link), by creating simple dynamic haptic virtual environments and understanding the performance limitations and causes of instabilities (direct/virtual coupling, friction, damping, time delays, sampling rate, sensor quantization, etc.) during rendering of different mechanical properties.
|Lecture notes||Will be distributed through the document repository before the lectures.|
|Literature||Abbott, J. and Okamura, A. (2005). Effects of position quantization and sampling rate on virtual-wall passivity. Robotics, IEEE Transactions on, 21(5):952 - 964.|
Adams, R. and Hannaford, B. (1999). Stable haptic interaction with virtual environments. Robotics and Automation, IEEE Transactions on, 15(3):465 -474.
Buerger, S. and Hogan, N. (2007). Complementary stability and loop shaping for improved human ndash;robot interaction. Robotics, IEEE Transactions on, 23(2):232 -244.
Burdea, G. and Brooks, F. (1996). Force and touch feedback for virtual reality. John Wiley & Sons New York NY.
Colgate, J. and Brown, J. (1994). Factors affecting the z-width of a haptic display. In Robotics and Automation, 1994. Proceedings., 1994 IEEE International Conference on, pages 3205 -3210 vol.4.
Diolaiti, N., Niemeyer, G., Barbagli, F., and Salisbury, J. (2006). Stability of haptic rendering: Discretization, quantization, time delay, and coulomb effects. Robotics, IEEE Transactions on, 22(2):256 -268.
Gillespie, R. and Cutkosky, M. (1996). Stable user-specific haptic rendering of the virtual wall. In Proceedings of the ASME International Mechanical Engineering Congress and Exhibition, volume 58, pages 397-406.
Hannaford, B. and Ryu, J.-H. (2002). Time-domain passivity control of haptic interfaces. Robotics and Automation, IEEE Transactions on, 18(1):1 -10.
Hashtrudi-Zaad, K. and Salcudean, S. (2001). Analysis of control architectures for teleoperation systems with impedance/admittance master and slave manipulators. The International Journal of Robotics Research, 20(6):419.
Hayward, V. and Astley, O. (1996). Performance measures for haptic interfaces. In ROBOTICS RESEARCH-INTERNATIONAL SYMPOSIUM-, volume 7, pages 195-206. Citeseer.
Hayward, V. and Maclean, K. (2007). Do it yourself haptics: part i. Robotics Automation Magazine, IEEE, 14(4):88 -104.
Leskovsky, P., Harders, M., and Szeekely, G. (2006). Assessing the fidelity of haptically rendered deformable objects. In Haptic Interfaces for Virtual Environment and Teleoperator Systems, 2006 14th Symposium on, pages 19 - 25.
MacLean, K. and Hayward, V. (2008). Do it yourself haptics: Part ii [tutorial]. Robotics Automation Magazine, IEEE, 15(1):104 -119.
Mahvash, M. and Hayward, V. (2003). Passivity-based high-fidelity haptic rendering of contact. In Robotics and Automation, 2003. Proceedings. ICRA '03. IEEE International Conference on, volume 3, pages 3722 - 3728 vol.3.
Mehling, J., Colgate, J., and Peshkin, M. (2005). Increasing the impedance range of a haptic display by adding electrical damping. In Eurohaptics Conference, 2005 and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, 2005. World Haptics 2005. First Joint, pages 257 - 262.
Okamura, A., Richard, C., and Cutkosky, M. (2002). Feeling is believing: Using a force-feedback joystick to teach dynamic systems. JOURNAL OF ENGINEERING EDUCATION-WASHINGTON-, 91(3):345-350.
O'Malley, M. and Goldfarb, M. (2004). The effect of virtual surface stiffness on the haptic perception of detail. Mechatronics, IEEE/ASME Transactions on, 9(2):448 -454.
Richard, C. and Cutkosky, M. (2000). The effects of real and computer generated friction on human performance in a targeting task. In Proceedings of the ASME Dynamic Systems and Control Division, volume 69, page 2.
Salisbury, K., Conti, F., and Barbagli, F. (2004). Haptic rendering: Introductory concepts. Computer Graphics and Applications, IEEE, 24(2):24-32.
Weir, D., Colgate, J., and Peshkin, M. (2008). Measuring and increasing z-width with active electrical damping. In Haptic interfaces for virtual environment and teleoperator systems, 2008. haptics 2008. symposium on, pages 169 -175.
Yasrebi, N. and Constantinescu, D. (2008). Extending the z-width of a haptic device using acceleration feedback. Haptics: Perception, Devices and Scenarios, pages 157-162.
|Prerequisites / Notice||Notice:|
The registration is limited to 26 students
There are 4 credit points for this lecture.
The lecture will be held in English.
The students are expected to have basic control knowledge from previous classes.
|636-0007-00L||Computational Systems Biology||W||6 credits||3V + 2U||J. Stelling|
|Abstract||Study of fundamental concepts, models and computational methods for the analysis of complex biological networks. Topics: Systems approaches in biology, biology and reaction network fundamentals, modeling and simulation approaches (topological, probabilistic, stoichiometric, qualitative, linear / nonlinear ODEs, stochastic), and systems analysis (complexity reduction, stability, identification).|
|Objective||The aim of this course is to provide an introductory overview of mathematical and computational methods for the modeling, simulation and analysis of biological networks.|
|Content||Biology has witnessed an unprecedented increase in experimental data and, correspondingly, an increased need for computational methods to analyze this data. The explosion of sequenced genomes, and subsequently, of bioinformatics methods for the storage, analysis and comparison of genetic sequences provides a prominent example. Recently, however, an additional area of research, captured by the label "Systems Biology", focuses on how networks, which are more than the mere sum of their parts' properties, establish biological functions. This is essentially a task of reverse engineering. The aim of this course is to provide an introductory overview of corresponding computational methods for the modeling, simulation and analysis of biological networks. We will start with an introduction into the basic units, functions and design principles that are relevant for biology at the level of individual cells. Making extensive use of example systems, the course will then focus on methods and algorithms that allow for the investigation of biological networks with increasing detail. These include (i) graph theoretical approaches for revealing large-scale network organization, (ii) probabilistic (Bayesian) network representations, (iii) structural network analysis based on reaction stoichiometries, (iv) qualitative methods for dynamic modeling and simulation (Boolean and piece-wise linear approaches), (v) mechanistic modeling using ordinary differential equations (ODEs) and finally (vi) stochastic simulation methods.|
|Literature||U. Alon, An introduction to systems biology. Chapman & Hall / CRC, 2006.|
Z. Szallasi et al. (eds.), System modeling in cellular biology. MIT Press, 2010.
B. Ingalls, Mathematical modeling in systems biology: an introduction. MIT Press, 2013
|» Any courses offered by the Departments of MAVT, ITET or INFK. Your tutor must agree to this choice.|
|GESS Science in Perspective|
|» Recommended GESS Science in Perspective (Type B) for D-MAVT.|
|» see GESS Science in Perspective: Language Courses ETH/UZH|
|» see GESS Science in Perspective: Type A: Enhancement of Reflection Capability|
|151-1014-00L||Semester Project Robotics, Systems and Control |
Only for Robotics, Systems and Control MSc.
The subject of the Semester Project and the choice of the supervisor (ETH-professor) are to be approved in advance by the tutor.
|Abstract||The semester project is designed to train the students in the solution of specific engineering problems. This makes use of the technical and social skills acquired during the master's program. Tutors propose the subject of the project, elaborate the project plan, and define the roadmap together with their students, as well as monitor the overall execution.|
|Objective||The semester project is designed to train the students in the solution of specific engineering problems. This makes use of the technical and social skills acquired during the master's program.|
|151-1090-00L||Industrial Internship |
Access to the company list and request for recognition under www.mavt.ethz.ch/praxis.
|O||8 credits||external organisers|
|Abstract||The main objective of the minimum twelve-week internship is to expose Master’s students to the industrial work environment. The aim of the Industrial Internship is to apply engineering knowledge to practical situations.|
|Objective||The aim of the Industrial Internship is to apply engineering knowledge to practical situations.|
|151-1016-00L||Master's Thesis Robotics, Systems and Control |
Students who fulfill the following criteria are allowed to begin with their Master's Thesis:
a. successful completion of the bachelor program;
b. fulfilling of any additional requirements necessary to gain admission to the master programme;
c. successful completion of the semester project;
d. achievement of 28 ECTS in the category "Core Courses".
The Master's Thesis must be approved in advance by the tutor and is supervised by a professor of ETH Zurich or an adjunct faculty of RSC.
To choose a titular professor as a supervisor, please contact the D-MAVT Student Administration.
|Abstract||Master's programs are concluded by the master's thesis. The thesis is aimed at enhancing the student's capability to work independently toward the solution of a theoretical or applied problem. The subject of the master's thesis, as well as the project plan and roadmap, are proposed by the tutor and further elaborated with the student.|
|Objective||The thesis is aimed at enhancing the student's capability to work independently toward the solution of a theoretical or applied problem.|